Novel methods of medical diagnostics based on vibrational spectroscopy and vibrational micro-spectroscopy (e.g., infrared and Raman) capture a spectral snapshot of the averaged biochemical composition of cells and tissues rather than relying on subjective and inefficient studies of cell morphology and tissue architectural features. For example, infrared micro-spectroscopy is now applied to the study of cell diseases (“Spectral Cytopathology” or “SCP”) and the study of tissue diseases (“Spectral Histopathology” or “SHP”). However, in both SCP and SHP, as well as other spectroscopy applications, researchers have observed the contamination of spectral patterns by certain artifacts that aggravate spectral analysis by causing a contaminated spectrum to be classified differently (in terms of chemical content) than it would be if uncontaminated.
Until recently, the source of the contamination was not completely understood, and it was considered that the contamination was due to chemical variations within the sample or instrument software malfunction.
More recently, the contamination has been attributed to the superimposition of reflective band shapes on absorbance band shapes due to Mie scattering by spherical particles, such as cellular nuclei or spherical cells, and the anomalous dispersion of the refractive index. The interaction of Mie scattering and the mixing of dispersive band shapes are known as Resonance Mie (“RMie”) scattering.
Mie scattering manifests as broad, undulating background features as can be seen in FIG. 3. Meanwhile, the RMie scattering artifacts are visible in FIG. 2. The bottom trace 21 represents an uncontaminated spectral band shape for biological tissue. Meanwhile, the top trace 22 depicts a spectral band shape strongly contaminated by reflective components, namely an intensity shift 23 and a frequency shift 24. The differences between the two traces in FIG. 2 indicate that the spectral distortions are independent of the chemical composition but depend, instead, on the morphology of the sample.
In addition to distorting SCP and SHP optical spectra, RMie scattering artifacts frequently occur with applications using Diffuse Reflectance Infrared Fourier Transform Spectroscopy (“DRIFTS”), Attenuated Total Reflectance (“ATR”) spectroscopy, Coherent Anti-Stokes Raman Spectroscopy (“CARS”), and other forms of spectroscopy in which the real and imaginary parts of the complex refractive index mix significantly. Thus, distorted band shapes result not only from the interaction between reflectance and absorbance but from anomalous dispersion of the refractive index relating to other physical parameters, such as the resonant and non-resonant signal components at play in CARS.
There have been ongoing attempts to compensate for distorted band shapes as arising from the superposition of reflective components onto the absorbance features of infrared spectra. In one approach, researchers attributed these effects to incorrect phase correction of the instrument control software. Other researchers have applied existing methods for removing Mie scattering distortions. For example, the Extended Multiplicative Signal Correction (“EMSC”) method iteratively corrected each spectrum of a dataset according to a “reference” spectrum, initially the mean spectrum of the dataset or an “artificial” spectrum (e.g., the spectrum of a pure protein matrix). In one iteration, a dataset of 1000 spectra produced 1000 corrected spectra. Then, each corrected spectrum was used as the reference for the subsequent iteration, thus requiring 1,000,000 correction runs. At about ten passes and computation times measured in days, a stable level of corrected output spectra was highly inefficient.
More recently, researchers have eliminated scattering and reflective band shapes from spectra by obtaining the reflective components (the “interference spectra”) via the Kramers-Kronig transform and the Mie scattering curves via the van Hulst equation. Then, the distorting components were subtracted from all the spectra in a dataset via EMSC. This process avoided the slow, iterative approach by carrying out a preliminary cluster analysis of the dataset and selecting the spectrum with the highest amide I frequencies in each cluster as the “uncontaminated” reference spectrum. This process also accounted for multiple interference spectra; however, the definition of such interference spectra added subjectivity to the process.